Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Thursday 18 June 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 149,576 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 2[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bed… 27 mk454hr East of E…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bla… 9 bb12fd North West
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bro… 11 br33ql London
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_can… 9 ws111jp Midlands
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_cit… 12 n15lz London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_enf… 7 en40dy London
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ham… 6 dl62uu North Eas…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_har… 24 ts232la North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_kin… 6 kt11eu London
## [90m# … with 149,566 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 91
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 12
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 5
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 0
## 104 2020-06-12 East of England 8
## 105 2020-06-13 East of England 3
## 106 2020-06-14 East of England 3
## 107 2020-06-15 East of England 4
## 108 2020-06-16 East of England 0
## 109 2020-03-01 London 0
## 110 2020-03-02 London 0
## 111 2020-03-03 London 0
## 112 2020-03-04 London 0
## 113 2020-03-05 London 0
## 114 2020-03-06 London 1
## 115 2020-03-07 London 1
## 116 2020-03-08 London 0
## 117 2020-03-09 London 1
## 118 2020-03-10 London 0
## 119 2020-03-11 London 7
## 120 2020-03-12 London 6
## 121 2020-03-13 London 10
## 122 2020-03-14 London 14
## 123 2020-03-15 London 10
## 124 2020-03-16 London 18
## 125 2020-03-17 London 25
## 126 2020-03-18 London 31
## 127 2020-03-19 London 25
## 128 2020-03-20 London 44
## 129 2020-03-21 London 50
## 130 2020-03-22 London 54
## 131 2020-03-23 London 64
## 132 2020-03-24 London 87
## 133 2020-03-25 London 113
## 134 2020-03-26 London 130
## 135 2020-03-27 London 130
## 136 2020-03-28 London 122
## 137 2020-03-29 London 147
## 138 2020-03-30 London 150
## 139 2020-03-31 London 181
## 140 2020-04-01 London 202
## 141 2020-04-02 London 190
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## 149 2020-04-10 London 170
## 150 2020-04-11 London 177
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## 152 2020-04-13 London 166
## 153 2020-04-14 London 144
## 154 2020-04-15 London 142
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## 156 2020-04-17 London 100
## 157 2020-04-18 London 101
## 158 2020-04-19 London 103
## 159 2020-04-20 London 95
## 160 2020-04-21 London 95
## 161 2020-04-22 London 109
## 162 2020-04-23 London 77
## 163 2020-04-24 London 71
## 164 2020-04-25 London 58
## 165 2020-04-26 London 53
## 166 2020-04-27 London 51
## 167 2020-04-28 London 43
## 168 2020-04-29 London 44
## 169 2020-04-30 London 40
## 170 2020-05-01 London 41
## 171 2020-05-02 London 40
## 172 2020-05-03 London 36
## 173 2020-05-04 London 30
## 174 2020-05-05 London 25
## 175 2020-05-06 London 37
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## 187 2020-05-18 London 9
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## 192 2020-05-23 London 6
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## 195 2020-05-26 London 12
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## 200 2020-05-31 London 6
## 201 2020-06-01 London 10
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## 205 2020-06-05 London 3
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## 210 2020-06-10 London 7
## 211 2020-06-11 London 5
## 212 2020-06-12 London 3
## 213 2020-06-13 London 3
## 214 2020-06-14 London 2
## 215 2020-06-15 London 1
## 216 2020-06-16 London 0
## 217 2020-03-01 Midlands 0
## 218 2020-03-02 Midlands 0
## 219 2020-03-03 Midlands 1
## 220 2020-03-04 Midlands 0
## 221 2020-03-05 Midlands 0
## 222 2020-03-06 Midlands 0
## 223 2020-03-07 Midlands 0
## 224 2020-03-08 Midlands 3
## 225 2020-03-09 Midlands 1
## 226 2020-03-10 Midlands 0
## 227 2020-03-11 Midlands 2
## 228 2020-03-12 Midlands 6
## 229 2020-03-13 Midlands 5
## 230 2020-03-14 Midlands 4
## 231 2020-03-15 Midlands 5
## 232 2020-03-16 Midlands 11
## 233 2020-03-17 Midlands 8
## 234 2020-03-18 Midlands 13
## 235 2020-03-19 Midlands 8
## 236 2020-03-20 Midlands 28
## 237 2020-03-21 Midlands 13
## 238 2020-03-22 Midlands 31
## 239 2020-03-23 Midlands 33
## 240 2020-03-24 Midlands 41
## 241 2020-03-25 Midlands 48
## 242 2020-03-26 Midlands 64
## 243 2020-03-27 Midlands 72
## 244 2020-03-28 Midlands 89
## 245 2020-03-29 Midlands 92
## 246 2020-03-30 Midlands 90
## 247 2020-03-31 Midlands 123
## 248 2020-04-01 Midlands 140
## 249 2020-04-02 Midlands 142
## 250 2020-04-03 Midlands 124
## 251 2020-04-04 Midlands 151
## 252 2020-04-05 Midlands 164
## 253 2020-04-06 Midlands 140
## 254 2020-04-07 Midlands 123
## 255 2020-04-08 Midlands 186
## 256 2020-04-09 Midlands 139
## 257 2020-04-10 Midlands 127
## 258 2020-04-11 Midlands 142
## 259 2020-04-12 Midlands 139
## 260 2020-04-13 Midlands 120
## 261 2020-04-14 Midlands 116
## 262 2020-04-15 Midlands 147
## 263 2020-04-16 Midlands 102
## 264 2020-04-17 Midlands 118
## 265 2020-04-18 Midlands 115
## 266 2020-04-19 Midlands 92
## 267 2020-04-20 Midlands 107
## 268 2020-04-21 Midlands 86
## 269 2020-04-22 Midlands 78
## 270 2020-04-23 Midlands 103
## 271 2020-04-24 Midlands 79
## 272 2020-04-25 Midlands 72
## 273 2020-04-26 Midlands 81
## 274 2020-04-27 Midlands 74
## 275 2020-04-28 Midlands 68
## 276 2020-04-29 Midlands 53
## 277 2020-04-30 Midlands 56
## 278 2020-05-01 Midlands 64
## 279 2020-05-02 Midlands 51
## 280 2020-05-03 Midlands 52
## 281 2020-05-04 Midlands 61
## 282 2020-05-05 Midlands 58
## 283 2020-05-06 Midlands 59
## 284 2020-05-07 Midlands 48
## 285 2020-05-08 Midlands 34
## 286 2020-05-09 Midlands 37
## 287 2020-05-10 Midlands 42
## 288 2020-05-11 Midlands 33
## 289 2020-05-12 Midlands 45
## 290 2020-05-13 Midlands 40
## 291 2020-05-14 Midlands 37
## 292 2020-05-15 Midlands 40
## 293 2020-05-16 Midlands 34
## 294 2020-05-17 Midlands 31
## 295 2020-05-18 Midlands 34
## 296 2020-05-19 Midlands 34
## 297 2020-05-20 Midlands 36
## 298 2020-05-21 Midlands 32
## 299 2020-05-22 Midlands 27
## 300 2020-05-23 Midlands 34
## 301 2020-05-24 Midlands 19
## 302 2020-05-25 Midlands 26
## 303 2020-05-26 Midlands 33
## 304 2020-05-27 Midlands 29
## 305 2020-05-28 Midlands 27
## 306 2020-05-29 Midlands 20
## 307 2020-05-30 Midlands 20
## 308 2020-05-31 Midlands 22
## 309 2020-06-01 Midlands 20
## 310 2020-06-02 Midlands 22
## 311 2020-06-03 Midlands 24
## 312 2020-06-04 Midlands 15
## 313 2020-06-05 Midlands 21
## 314 2020-06-06 Midlands 20
## 315 2020-06-07 Midlands 16
## 316 2020-06-08 Midlands 15
## 317 2020-06-09 Midlands 17
## 318 2020-06-10 Midlands 14
## 319 2020-06-11 Midlands 13
## 320 2020-06-12 Midlands 12
## 321 2020-06-13 Midlands 6
## 322 2020-06-14 Midlands 14
## 323 2020-06-15 Midlands 10
## 324 2020-06-16 Midlands 0
## 325 2020-03-01 North East and Yorkshire 0
## 326 2020-03-02 North East and Yorkshire 0
## 327 2020-03-03 North East and Yorkshire 0
## 328 2020-03-04 North East and Yorkshire 0
## 329 2020-03-05 North East and Yorkshire 0
## 330 2020-03-06 North East and Yorkshire 0
## 331 2020-03-07 North East and Yorkshire 0
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## 339 2020-03-15 North East and Yorkshire 2
## 340 2020-03-16 North East and Yorkshire 3
## 341 2020-03-17 North East and Yorkshire 1
## 342 2020-03-18 North East and Yorkshire 2
## 343 2020-03-19 North East and Yorkshire 6
## 344 2020-03-20 North East and Yorkshire 5
## 345 2020-03-21 North East and Yorkshire 6
## 346 2020-03-22 North East and Yorkshire 7
## 347 2020-03-23 North East and Yorkshire 9
## 348 2020-03-24 North East and Yorkshire 8
## 349 2020-03-25 North East and Yorkshire 18
## 350 2020-03-26 North East and Yorkshire 21
## 351 2020-03-27 North East and Yorkshire 28
## 352 2020-03-28 North East and Yorkshire 35
## 353 2020-03-29 North East and Yorkshire 38
## 354 2020-03-30 North East and Yorkshire 64
## 355 2020-03-31 North East and Yorkshire 60
## 356 2020-04-01 North East and Yorkshire 67
## 357 2020-04-02 North East and Yorkshire 74
## 358 2020-04-03 North East and Yorkshire 100
## 359 2020-04-04 North East and Yorkshire 105
## 360 2020-04-05 North East and Yorkshire 92
## 361 2020-04-06 North East and Yorkshire 96
## 362 2020-04-07 North East and Yorkshire 102
## 363 2020-04-08 North East and Yorkshire 107
## 364 2020-04-09 North East and Yorkshire 111
## 365 2020-04-10 North East and Yorkshire 117
## 366 2020-04-11 North East and Yorkshire 98
## 367 2020-04-12 North East and Yorkshire 84
## 368 2020-04-13 North East and Yorkshire 94
## 369 2020-04-14 North East and Yorkshire 107
## 370 2020-04-15 North East and Yorkshire 96
## 371 2020-04-16 North East and Yorkshire 103
## 372 2020-04-17 North East and Yorkshire 88
## 373 2020-04-18 North East and Yorkshire 95
## 374 2020-04-19 North East and Yorkshire 88
## 375 2020-04-20 North East and Yorkshire 100
## 376 2020-04-21 North East and Yorkshire 76
## 377 2020-04-22 North East and Yorkshire 84
## 378 2020-04-23 North East and Yorkshire 63
## 379 2020-04-24 North East and Yorkshire 72
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## 382 2020-04-27 North East and Yorkshire 65
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## 385 2020-04-30 North East and Yorkshire 57
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## 387 2020-05-02 North East and Yorkshire 48
## 388 2020-05-03 North East and Yorkshire 40
## 389 2020-05-04 North East and Yorkshire 49
## 390 2020-05-05 North East and Yorkshire 40
## 391 2020-05-06 North East and Yorkshire 51
## 392 2020-05-07 North East and Yorkshire 45
## 393 2020-05-08 North East and Yorkshire 42
## 394 2020-05-09 North East and Yorkshire 44
## 395 2020-05-10 North East and Yorkshire 40
## 396 2020-05-11 North East and Yorkshire 29
## 397 2020-05-12 North East and Yorkshire 27
## 398 2020-05-13 North East and Yorkshire 28
## 399 2020-05-14 North East and Yorkshire 30
## 400 2020-05-15 North East and Yorkshire 32
## 401 2020-05-16 North East and Yorkshire 35
## 402 2020-05-17 North East and Yorkshire 26
## 403 2020-05-18 North East and Yorkshire 29
## 404 2020-05-19 North East and Yorkshire 27
## 405 2020-05-20 North East and Yorkshire 21
## 406 2020-05-21 North East and Yorkshire 33
## 407 2020-05-22 North East and Yorkshire 22
## 408 2020-05-23 North East and Yorkshire 18
## 409 2020-05-24 North East and Yorkshire 25
## 410 2020-05-25 North East and Yorkshire 21
## 411 2020-05-26 North East and Yorkshire 21
## 412 2020-05-27 North East and Yorkshire 22
## 413 2020-05-28 North East and Yorkshire 20
## 414 2020-05-29 North East and Yorkshire 25
## 415 2020-05-30 North East and Yorkshire 20
## 416 2020-05-31 North East and Yorkshire 19
## 417 2020-06-01 North East and Yorkshire 16
## 418 2020-06-02 North East and Yorkshire 22
## 419 2020-06-03 North East and Yorkshire 22
## 420 2020-06-04 North East and Yorkshire 17
## 421 2020-06-05 North East and Yorkshire 17
## 422 2020-06-06 North East and Yorkshire 20
## 423 2020-06-07 North East and Yorkshire 13
## 424 2020-06-08 North East and Yorkshire 11
## 425 2020-06-09 North East and Yorkshire 11
## 426 2020-06-10 North East and Yorkshire 16
## 427 2020-06-11 North East and Yorkshire 6
## 428 2020-06-12 North East and Yorkshire 8
## 429 2020-06-13 North East and Yorkshire 10
## 430 2020-06-14 North East and Yorkshire 11
## 431 2020-06-15 North East and Yorkshire 7
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## 449 2020-03-17 North West 4
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## 451 2020-03-19 North West 7
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## 454 2020-03-22 North West 13
## 455 2020-03-23 North West 16
## 456 2020-03-24 North West 21
## 457 2020-03-25 North West 21
## 458 2020-03-26 North West 29
## 459 2020-03-27 North West 35
## 460 2020-03-28 North West 28
## 461 2020-03-29 North West 46
## 462 2020-03-30 North West 67
## 463 2020-03-31 North West 52
## 464 2020-04-01 North West 86
## 465 2020-04-02 North West 96
## 466 2020-04-03 North West 95
## 467 2020-04-04 North West 98
## 468 2020-04-05 North West 102
## 469 2020-04-06 North West 100
## 470 2020-04-07 North West 134
## 471 2020-04-08 North West 127
## 472 2020-04-09 North West 119
## 473 2020-04-10 North West 117
## 474 2020-04-11 North West 139
## 475 2020-04-12 North West 126
## 476 2020-04-13 North West 129
## 477 2020-04-14 North West 131
## 478 2020-04-15 North West 114
## 479 2020-04-16 North West 134
## 480 2020-04-17 North West 98
## 481 2020-04-18 North West 113
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## 483 2020-04-20 North West 83
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## 485 2020-04-22 North West 86
## 486 2020-04-23 North West 85
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## 488 2020-04-25 North West 65
## 489 2020-04-26 North West 55
## 490 2020-04-27 North West 54
## 491 2020-04-28 North West 57
## 492 2020-04-29 North West 62
## 493 2020-04-30 North West 59
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## 495 2020-05-02 North West 56
## 496 2020-05-03 North West 55
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## 498 2020-05-05 North West 48
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## 500 2020-05-07 North West 49
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## 503 2020-05-10 North West 41
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## 505 2020-05-12 North West 38
## 506 2020-05-13 North West 25
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## 524 2020-05-31 North West 13
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## 529 2020-06-05 North West 15
## 530 2020-06-06 North West 23
## 531 2020-06-07 North West 17
## 532 2020-06-08 North West 19
## 533 2020-06-09 North West 15
## 534 2020-06-10 North West 13
## 535 2020-06-11 North West 14
## 536 2020-06-12 North West 5
## 537 2020-06-13 North West 7
## 538 2020-06-14 North West 11
## 539 2020-06-15 North West 14
## 540 2020-06-16 North West 4
## 541 2020-03-01 South East 0
## 542 2020-03-02 South East 0
## 543 2020-03-03 South East 1
## 544 2020-03-04 South East 0
## 545 2020-03-05 South East 1
## 546 2020-03-06 South East 0
## 547 2020-03-07 South East 0
## 548 2020-03-08 South East 1
## 549 2020-03-09 South East 1
## 550 2020-03-10 South East 1
## 551 2020-03-11 South East 1
## 552 2020-03-12 South East 0
## 553 2020-03-13 South East 1
## 554 2020-03-14 South East 1
## 555 2020-03-15 South East 5
## 556 2020-03-16 South East 8
## 557 2020-03-17 South East 7
## 558 2020-03-18 South East 10
## 559 2020-03-19 South East 9
## 560 2020-03-20 South East 14
## 561 2020-03-21 South East 7
## 562 2020-03-22 South East 25
## 563 2020-03-23 South East 20
## 564 2020-03-24 South East 22
## 565 2020-03-25 South East 29
## 566 2020-03-26 South East 35
## 567 2020-03-27 South East 34
## 568 2020-03-28 South East 36
## 569 2020-03-29 South East 55
## 570 2020-03-30 South East 58
## 571 2020-03-31 South East 65
## 572 2020-04-01 South East 66
## 573 2020-04-02 South East 55
## 574 2020-04-03 South East 72
## 575 2020-04-04 South East 80
## 576 2020-04-05 South East 82
## 577 2020-04-06 South East 88
## 578 2020-04-07 South East 100
## 579 2020-04-08 South East 83
## 580 2020-04-09 South East 104
## 581 2020-04-10 South East 88
## 582 2020-04-11 South East 88
## 583 2020-04-12 South East 88
## 584 2020-04-13 South East 84
## 585 2020-04-14 South East 65
## 586 2020-04-15 South East 72
## 587 2020-04-16 South East 56
## 588 2020-04-17 South East 86
## 589 2020-04-18 South East 57
## 590 2020-04-19 South East 70
## 591 2020-04-20 South East 86
## 592 2020-04-21 South East 50
## 593 2020-04-22 South East 54
## 594 2020-04-23 South East 57
## 595 2020-04-24 South East 64
## 596 2020-04-25 South East 51
## 597 2020-04-26 South East 51
## 598 2020-04-27 South East 40
## 599 2020-04-28 South East 40
## 600 2020-04-29 South East 47
## 601 2020-04-30 South East 29
## 602 2020-05-01 South East 37
## 603 2020-05-02 South East 36
## 604 2020-05-03 South East 17
## 605 2020-05-04 South East 35
## 606 2020-05-05 South East 29
## 607 2020-05-06 South East 25
## 608 2020-05-07 South East 27
## 609 2020-05-08 South East 26
## 610 2020-05-09 South East 28
## 611 2020-05-10 South East 19
## 612 2020-05-11 South East 25
## 613 2020-05-12 South East 27
## 614 2020-05-13 South East 18
## 615 2020-05-14 South East 32
## 616 2020-05-15 South East 24
## 617 2020-05-16 South East 22
## 618 2020-05-17 South East 18
## 619 2020-05-18 South East 22
## 620 2020-05-19 South East 12
## 621 2020-05-20 South East 22
## 622 2020-05-21 South East 14
## 623 2020-05-22 South East 17
## 624 2020-05-23 South East 21
## 625 2020-05-24 South East 17
## 626 2020-05-25 South East 13
## 627 2020-05-26 South East 19
## 628 2020-05-27 South East 18
## 629 2020-05-28 South East 12
## 630 2020-05-29 South East 20
## 631 2020-05-30 South East 8
## 632 2020-05-31 South East 10
## 633 2020-06-01 South East 11
## 634 2020-06-02 South East 13
## 635 2020-06-03 South East 17
## 636 2020-06-04 South East 11
## 637 2020-06-05 South East 11
## 638 2020-06-06 South East 10
## 639 2020-06-07 South East 11
## 640 2020-06-08 South East 7
## 641 2020-06-09 South East 9
## 642 2020-06-10 South East 10
## 643 2020-06-11 South East 5
## 644 2020-06-12 South East 5
## 645 2020-06-13 South East 4
## 646 2020-06-14 South East 6
## 647 2020-06-15 South East 3
## 648 2020-06-16 South East 1
## 649 2020-03-01 South West 0
## 650 2020-03-02 South West 0
## 651 2020-03-03 South West 0
## 652 2020-03-04 South West 0
## 653 2020-03-05 South West 0
## 654 2020-03-06 South West 0
## 655 2020-03-07 South West 0
## 656 2020-03-08 South West 0
## 657 2020-03-09 South West 0
## 658 2020-03-10 South West 0
## 659 2020-03-11 South West 1
## 660 2020-03-12 South West 0
## 661 2020-03-13 South West 0
## 662 2020-03-14 South West 1
## 663 2020-03-15 South West 0
## 664 2020-03-16 South West 0
## 665 2020-03-17 South West 2
## 666 2020-03-18 South West 2
## 667 2020-03-19 South West 5
## 668 2020-03-20 South West 3
## 669 2020-03-21 South West 6
## 670 2020-03-22 South West 9
## 671 2020-03-23 South West 9
## 672 2020-03-24 South West 7
## 673 2020-03-25 South West 9
## 674 2020-03-26 South West 11
## 675 2020-03-27 South West 13
## 676 2020-03-28 South West 21
## 677 2020-03-29 South West 18
## 678 2020-03-30 South West 23
## 679 2020-03-31 South West 23
## 680 2020-04-01 South West 22
## 681 2020-04-02 South West 23
## 682 2020-04-03 South West 30
## 683 2020-04-04 South West 42
## 684 2020-04-05 South West 32
## 685 2020-04-06 South West 34
## 686 2020-04-07 South West 39
## 687 2020-04-08 South West 47
## 688 2020-04-09 South West 24
## 689 2020-04-10 South West 46
## 690 2020-04-11 South West 43
## 691 2020-04-12 South West 23
## 692 2020-04-13 South West 27
## 693 2020-04-14 South West 24
## 694 2020-04-15 South West 32
## 695 2020-04-16 South West 29
## 696 2020-04-17 South West 33
## 697 2020-04-18 South West 25
## 698 2020-04-19 South West 31
## 699 2020-04-20 South West 26
## 700 2020-04-21 South West 26
## 701 2020-04-22 South West 23
## 702 2020-04-23 South West 17
## 703 2020-04-24 South West 19
## 704 2020-04-25 South West 15
## 705 2020-04-26 South West 27
## 706 2020-04-27 South West 13
## 707 2020-04-28 South West 17
## 708 2020-04-29 South West 15
## 709 2020-04-30 South West 26
## 710 2020-05-01 South West 6
## 711 2020-05-02 South West 7
## 712 2020-05-03 South West 10
## 713 2020-05-04 South West 17
## 714 2020-05-05 South West 14
## 715 2020-05-06 South West 19
## 716 2020-05-07 South West 16
## 717 2020-05-08 South West 6
## 718 2020-05-09 South West 11
## 719 2020-05-10 South West 5
## 720 2020-05-11 South West 8
## 721 2020-05-12 South West 7
## 722 2020-05-13 South West 7
## 723 2020-05-14 South West 6
## 724 2020-05-15 South West 4
## 725 2020-05-16 South West 4
## 726 2020-05-17 South West 6
## 727 2020-05-18 South West 4
## 728 2020-05-19 South West 6
## 729 2020-05-20 South West 1
## 730 2020-05-21 South West 9
## 731 2020-05-22 South West 6
## 732 2020-05-23 South West 6
## 733 2020-05-24 South West 3
## 734 2020-05-25 South West 8
## 735 2020-05-26 South West 11
## 736 2020-05-27 South West 5
## 737 2020-05-28 South West 10
## 738 2020-05-29 South West 7
## 739 2020-05-30 South West 3
## 740 2020-05-31 South West 2
## 741 2020-06-01 South West 7
## 742 2020-06-02 South West 2
## 743 2020-06-03 South West 5
## 744 2020-06-04 South West 2
## 745 2020-06-05 South West 2
## 746 2020-06-06 South West 1
## 747 2020-06-07 South West 3
## 748 2020-06-08 South West 3
## 749 2020-06-09 South West 0
## 750 2020-06-10 South West 0
## 751 2020-06-11 South West 2
## 752 2020-06-12 South West 2
## 753 2020-06-13 South West 2
## 754 2020-06-14 South West 0
## 755 2020-06-15 South West 0
## 756 2020-06-16 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Wednesday 17 Jun 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -9.0853 -2.4464 -0.2367 2.7128 4.6601
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.980e+00 5.193e-02 95.90 <2e-16 ***
## note_lag 1.135e-05 5.153e-07 22.04 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.17979)
##
## Null deviance: 5302.68 on 46 degrees of freedom
## Residual deviance: 471.46 on 45 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 145.457216 1.000011
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 131.23844 160.869911
## note_lag 1.00001 1.000012
Rsq(lag_mod)
## [1] 0.9110902
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.3.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.13
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.4.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.1
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0 ellipsis_0.3.1
## [5] rprojroot_1.3-2 snakecase_0.11.0 fs_1.4.1 rstudioapi_0.11
## [9] farver_2.0.3 fansi_0.4.1 splines_3.6.3 knitr_1.28
## [13] jsonlite_1.6.1 broom_0.5.6 dbplyr_1.4.4 compiler_3.6.3
## [17] httr_1.4.1 backports_1.1.8 assertthat_0.2.1 Matrix_1.2-18
## [21] cli_2.0.2 htmltools_0.5.0 prettyunits_1.1.1 tools_3.6.3
## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
## [29] cellranger_1.1.0 vctrs_0.3.1 nlme_3.1-144 matchmaker_0.1.1
## [33] crosstalk_1.1.0.1 xfun_0.14 ps_1.3.3 openxlsx_4.1.5
## [37] lifecycle_0.2.0 rstatix_0.5.0 MASS_7.3-51.5 scales_1.1.1
## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-24
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
## [53] evaluate_0.14 lattice_0.20-38 labeling_0.3 htmlwidgets_1.5.1
## [57] cowplot_1.0.0 processx_3.4.2 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.4 haven_2.3.1 foreign_0.8-75 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-8 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-8 utf8_1.1.4 rmarkdown_2.2
## [77] viridis_0.5.1 grid_3.6.3 readxl_1.3.1 data.table_1.12.8
## [81] blob_1.2.1 callr_3.4.3 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0